mlsanityData to 14 Oct 2026

Automated Sleep Staging via Parallel Frequency-Cut Attention

arXiv 2204.03173 ↗First posted 7 Apr 2022cs.LG cs.AI eess.SPcited by 7

Not on the map. Why?

Abstract (arXiv)

This paper proposes a novel framework for automatically capturing the time-frequency nature of electroencephalogram (EEG) signals of human sleep based on the authoritative sleep medicine guidance. The framework consists of two parts: the first part extracts informative features by partitioning the input EEG spectrograms into a sequence of time-frequency patches. The second part is constituted by an attention-based architecture to efficiently search for the correlation between partitioned time-frequency patches and defining factors of sleep stages in parallel. The proposed pipeline is validated on the Sleep Heart Health Study dataset with new state-of-the-art results for the stages wake, N2, and N3, obtaining respective F1 scores of 0.93, 0.88, and 0.87, with only EEG signals used. The proposed method also has a high inter-rater reliability of 0.80 kappa. We also visualize the correspondence between sleep staging decisions and features extracted by the proposed method, providing strong interpretability for our model.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 15 of its 61 references that are in the map. First posted on arXiv 7 Apr 2022.

Builds on

15 of 61 references.

References · 15

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  4. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  5. 2014Going Deeper with Convolutionscited by 4,241
  6. 2017Axiomatic Attribution for Deep Networkscited by 3,384
  7. 2020Transformer Interpretability Beyond Attention Visualizationcited by 627
  8. 2017DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEGcited by 157
  9. 2018SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Stagingcited by 91
  10. 2020XSleepNet: Multi-View Sequential Model for Automatic Sleep Stagingcited by 88
  11. 2021SleepTransformer: Automatic Sleep Staging with Interpretability and Uncertainty Quantificationcited by 84
  12. 2018Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classificationcited by 42
3 more
  1. 2021DeepSleepNet-Lite: A Simplified Automatic Sleep Stage Scoring Model with Uncertainty Estimatescited by 26
  2. 2021Ensemble of Convolution Neural Networks on Heterogeneous Signals for Sleep Stage Scoringcited by 2
  3. 2018A Deep Learning Approach with an Attention Mechanism for Automatic Sleep Stage Classificationcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 3; 2025: 2; 2026: 1.

7 papers.

  1. 2024Fredformer: Frequency Debiased Transformer for Time Series Forecastingcited by 45
  2. 2025Tokenizing Single-Channel EEG with Time-Frequency Motif Learningcited by 17
  3. 2024Dual-TSST: A Dual-Branch Temporal-Spectral-Spatial Transformer Model for EEG Decodingcited by 6
  4. 2022A multi-level interpretable sleep stage scoring system by infusing experts' knowledge into a deep network architecturecited by 4
  5. 2024SplitSEE: A Splittable Self-supervised Framework for Single-Channel EEG Representation Learningcited by 3
  6. 2025Transformer-based EEG Decoding: A Surveycited by 3
  7. 2026Atoms of Thought: Universal EEG Representation Learning with Microstatescited by 0